» Articles » PMID: 17012394

Experimental and Computational Assessment of Conditionally Essential Genes in Escherichia Coli

Overview
Journal J Bacteriol
Specialty Microbiology
Date 2006 Oct 3
PMID 17012394
Citations 155
Authors
Affiliations
Soon will be listed here.
Abstract

Genome-wide gene essentiality data sets are becoming available for Escherichia coli, but these data sets have yet to be analyzed in the context of a genome scale model. Here, we present an integrative model-driven analysis of the Keio E. coli mutant collection screened in this study on glycerol-supplemented minimal medium. Out of 3,888 single-deletion mutants tested, 119 mutants were unable to grow on glycerol minimal medium. These conditionally essential genes were then evaluated using a genome scale metabolic and transcriptional-regulatory model of E. coli, and it was found that the model made the correct prediction in approximately 91% of the cases. The discrepancies between model predictions and experimental results were analyzed in detail to indicate where model improvements could be made or where the current literature lacks an explanation for the observed phenotypes. The identified set of essential genes and their model-based analysis indicates that our current understanding of the roles these essential genes play is relatively clear and complete. Furthermore, by analyzing the data set in terms of metabolic subsystems across multiple genomes, we can project which metabolic pathways are likely to play equally important roles in other organisms. Overall, this work establishes a paradigm that will drive model enhancement while simultaneously generating hypotheses that will ultimately lead to a better understanding of the organism.

Citing Articles

Data-driven discovery of the interplay between genetic and environmental factors in bacterial growth.

Aida H, Ying B Commun Biol. 2024; 7(1):1691.

PMID: 39719455 PMC: 11668901. DOI: 10.1038/s42003-024-07347-3.


Genetic requirements for uropathogenic proliferation in the bladder cell infection cycle.

Mediati D, Blair T, Costas A, Monahan L, Soderstrom B, Charles I mSystems. 2024; 9(10):e0038724.

PMID: 39287381 PMC: 11495030. DOI: 10.1128/msystems.00387-24.


Defects in the central metabolism prevent thymineless death in Escherichia coli, while still allowing significant protein synthesis.

Khan S, Kuzminov A Genetics. 2024; 228(3).

PMID: 39212478 PMC: 11538421. DOI: 10.1093/genetics/iyae142.


Synthetic lethality and the minimal genome size problem.

Rahiminejad S, De Sanctis B, Pevzner P, Mushegian A mSphere. 2024; 9(7):e0013924.

PMID: 38904396 PMC: 11288024. DOI: 10.1128/msphere.00139-24.


Genome-scale model of predicts gene essentialities and reveals metabolic capabilities.

Leonidou N, Ostyn L, Coenye T, Crabbe A, Drager A Microbiol Spectr. 2024; 12(6):e0400623.

PMID: 38652457 PMC: 11237427. DOI: 10.1128/spectrum.04006-23.


References
1.
Covert M, Knight E, Reed J, Herrgard M, Palsson B . Integrating high-throughput and computational data elucidates bacterial networks. Nature. 2004; 429(6987):92-6. DOI: 10.1038/nature02456. View

2.
Levy S, Zeng G, Danchin A . Cyclic AMP synthesis in Escherichia coli strains bearing known deletions in the pts phosphotransferase operon. Gene. 1990; 86(1):27-33. DOI: 10.1016/0378-1119(90)90110-d. View

3.
Covert M, Schilling C, Palsson B . Regulation of gene expression in flux balance models of metabolism. J Theor Biol. 2001; 213(1):73-88. DOI: 10.1006/jtbi.2001.2405. View

4.
Chen K, Pachter L . Bioinformatics for whole-genome shotgun sequencing of microbial communities. PLoS Comput Biol. 2005; 1(2):106-12. PMC: 1185649. DOI: 10.1371/journal.pcbi.0010024. View

5.
Ito M, Baba T, Mori H, Mori H . Functional analysis of 1440 Escherichia coli genes using the combination of knock-out library and phenotype microarrays. Metab Eng. 2005; 7(4):318-27. DOI: 10.1016/j.ymben.2005.06.004. View